# Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples Source: [Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:44:30.600Z Authors: Marcus Soll, Tobias Hinz, Sven Magg, Stefan Wermter Published: 2019-08-21T14:50:13.000Z ## Abstract Adversarial examples are artificially modified input samples which lead to misclassifications, while not being detectable by humans. These adversarial examples are a challenge for many tasks such as image and text classification, especially as research shows that many adversarial examples are transferable between different classifiers. In this work, we evaluate the performance of a popular defensive strategy for adversarial examples called defensive distillation, which can be successful in hardening neural networks against adversarial examples in the image domain. However, instead of applying defensive distillation to networks for image classification, we examine, for the first time, its performance on text classification tasks and also evaluate its effect on the transferability of adversarial text examples. Our results indicate that defensive distillation only has a minimal impact on text classifying neural networks and does neither help with increasing their robustness against adversarial examples nor prevent the transferability of adversarial examples between neural networks. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()